Large variations of dissolved carbon occurred in small ponds within an agricultural watershed.

Dissolved organic carbon (DOC) and dissolved inorganic carbon (DIC) significantly affect the aquatic carbon budget and ecosystem functions. Small ponds are abundant globally and widely distributed especially in agricultural watersheds, however, the variability of DOC and DIC, along with their driving factors, remains poorly understood, which likely hampers the understanding of carbon cycle of inland waters. The presented study was designed to fill the knowledge gap based on a detailed year-long field investigation via examining DOC and DIC concentrations across ponds with differing functionalities (e.g. sewage ponds, irrigation ponds, and natural ponds) of a typical agricultural watershed in eastern China. Our results found a pronounced impact of human activities on pond DOC and DIC, with higher DOC occurring in sewage ponds (10.84 ±â€¯2.83 mg L-1) and irrigation ponds (9.09 ±â€¯2.57 mg L-1) and peak DIC in irrigation ponds (20.36 ±â€¯2.49 mg L-1) compared to that at natural ponds (DOC: 7.54 ±â€¯2.55 mg L-1; DIC: 11.16 ±â€¯3.85 mg L-1) with less human activity. The positive correlations between DOC/DIC and key environmental variables (e.g. nutrients and chlorophyll-a) further demonstrated that human activity can either directly increase the carbon concentrations via pollutant discharge, or indirectly increase DOC concentration via stimulating primary production. Meanwhile, field measurements found precipitation and temperature play roles in determining the carbon variability. Specifically, precipitation increased the DOC of these ponds via enhancing land-based carbon inputs, and decrease the DIC of irrigation ponds via diluting. Temperature can influence the carbon dynamics through increasing primary productivity and metabolism. Our study underscores the roles of human and natural influences in determining the large variations of DOC and DIC in small ponds, which should be considered to better understand the carbon dynamic variability of human-impacted small aquatic systems.

Multiple-model based simulation of urban atmospheric methane concentration and the attributions to its seasonal variations: A case study in Hangzhou megacity, China.

Cities are treated as global methane (CH4) emission hotspots and the monitoring of atmospheric CH4 concentration in cities is necessary to evaluate anthropogenic CH4 emissions. However, the continuous and in-situ observation sites within cities are still sparsely distributed in the largest CH4 emitter as of China, and although obvious seasonal variations of atmospheric CH4 concentrations have been observed in cities worldwide, questions regarding the drivers for their temporal variations still have not been well addressed. Therefore, to quantify the contributions to seasonal variations of atmospheric CH4 concentrations, year-round CH4 concentration observations from 1st December 2020 to 30th November 2021 were conducted in Hangzhou megacity, China, and three models were chosen to simulate urban atmospheric CH4 concentration and partition its drivers including machine learning based Random Forest (RF) model, atmospheric transport processes based numerical model (WRF-STILT), and regression analysis based Multiple Linear Regression (MLR) model. The findings are as follows: (1) the atmospheric CH4 concentration showed obvious seasonal variations and were different with previous observations in other cities, the seasonality were 5.8 ppb, 21.1 ppb, and 50.1 ppb between spring-winter, summer-winter and autumn-winter, respectively, where the CH4 background contributed by -8.1 ppb, -44.6 ppb, and -1.0 ppb, respectively, and the CH4 enhancements contributed by 13.9 ppb, 65.7 ppb, and 51.1 ppb. (2) The RF model showed the highest accuracy in simulating CH4 concentrations, followed by MLR model and WRF-STILT model. (3) We further partition contributions from different factors, results showed the largest contribution was from temperature-induced increase in microbial process based CH4 emissions including waste treatment and wetland, which ranged from 38.1 to 76.3 ppb when comparing different seasons with winter. The second largest contribution was from seasonal boundary layer height (BLH) variations, which ranged from -13.4 to -6.3 ppb. And the temperature induced seasonal CH4 emission and enhancement variations were overwhelming BLH changes and other meteorological parameters.

Spatiotemporal heterogeneity of lake carbon dioxide flux leads to substantial uncertainties in regional upscaling estimates.

Limited field samplings result in significant uncertainties in regional and global estimates of lake carbon dioxide (CO2) emissions. However, quantitative analysis of uncertainty in regional lake CO2 emission estimates remains unclear. In this study, we utilized satellite data to estimate carbon dioxide flux from 113 eastern China lakes, revealing substantial spatial and temporal variations in flux, averaging 18.07 ± 81.83 mg m-2 d-1. Additionally, satellite-estimated total CO2 effluxes indicated previous upscaling studies had overestimated total CO2 effluxes from these studied lakes by approximately 3-11 times, primarily due to substantial variations in lake CO2 fluxes. Insufficient sampling resolution resulted in considerable uncertainty in upscaling estimations. Temporal variations in carbon dioxide contributed greater upscaling uncertainties than spatial variations in carbon dioxide. To capture the dynamics of lake CO2, increasing the number of sampling points and events is necessary as lake size decreases and trophic state increases. Finally, we propose a prediction for the optimal sampling resolution based on lake area and trophic state, recommending an average of 5 points per lake and bi-monthly sampling as the ideal resolution for similar shallow eutrophic lakes. This approach has been validated as effective in lakes across North America and Europe. We believe that future global-scale lake carbon budget estimates would benefit from field observations conducted at more reasonable sampling points and frequency.

Ecological water diversion activity changes the fate of carbon in a eutrophic lake.

Lake eutrophication mitigation measures have been implemented by ecological water diversion, however, the responses of carbon cycle to the human-derived hydrologic process still remains unclear. With a famous river-to-lake water diversion activity at eutrophic Lake Taihu, we attempted to fill the knowledge gap with integrative field measurements (2011-2017) of gas carbon (CO2 and CH4) flux, including CO2-equivalent, and dissolved carbon (DOC and DIC) at water-receiving zone and reference zone. Overall, results showed the artificial water diversion activity increased gas carbon emissions. At water-receiving zone, total gas carbon (expressed as CO2-equivalent) emissions increased significantly due to the occurring of water diversion, with CO2 flux increasing from 9.31 ± 16.28 to 18.16 ± 12.96 mmol C m-2 d-1. Meanwhile, CH4 emissions at water-receiving zone (0.06 ± 0.05 mmol C m-2 d-1) was double of that at reference zone. Water diversion decreased DOC but increased DIC especially at inflowing river mouth. Temporal variability of carbon emissions and dissolved carbon were linked to water temperature, chlorophyll a, and nutrient, but less or negligible dependency on these environment variables were found with diversion occurring. Water diversion may increase gas carbon production via stimulating DOC mineralization with nutrient enrichment, which potentially contribute to increasing carbon emissions and decreasing DOC at the same time and the significant correlation between CO2 flux and CH4 flux. Our study provided new insights into carbon biogeochemical processes, which may help to predict carbon fate under hydrologic changes of lakes.

Methane ebullition fluxes and temperature sensitivity in a shallow lake.

Inland waters are important sources of atmospheric methane (CH4), with a major contribution from the CH4 ebullition pathway. However, there is still a lack of CH4 ebullition flux (eFCH4) and their temperature sensitivity (Q10) in shallow lakes, which might lead to large uncertainties in CH4 emission response from aquatic to climate and environmental change. Herein, the magnitude and regulatory of two CH4 pathways (ebullition and diffusion) were studied in subtropical Lake Chaohu, China, using the real-time portable greenhouse gas (GHG) analyzer-floating chamber method at 18 sites over four seasons. eFCH4 (12.06 ± 4.10 nmol m-2 s-1) was the dominant contributing pathway (73.0 %) to the two CH4 emission pathways in Lake Chaohu. The whole-lake mass balance calculation demonstrated that 56.6 % of the CH4 emitted from the sediment escaped through the ebullition pathway. eFCH4 was significantly higher in the western (WL: 16.54 ± 22.22 nmol m-2 s-1) and eastern lake zones (EL: 11.89 ± 15.43 nmol m-2 s-1) than in the middle lake zone (ML: 8.86 ± 13.78 nmol m-2 s-1; p < 0.05) and were significantly higher in the nearshore lake zone (NL: 15.94 ± 19.58 nmol m-2 s-1) than in the pelagic lake zone (PL: 6.64 ± 12.37 nmol m-2 s-1; p < 0.05). eFCH4 was significantly higher in summer (32.12 ± 13.82 nmol m-2 s-1) than in other seasons (p < 0.05). eFCH4 had a strong temperature dependence. Sediment total organic carbon (STOC) is an important ecosystem level Q10 driver of eFCH4. The meta-analysis also verified that across ecosystems the ecosystem-level Q10 of eFCH4 was significantly positively correlated with STOC and latitude (p < 0.05). This study suggests that eFCH4 will become increasingly crucial in shallow lake ecosystems as climate change and human activities increase. The potential increase in ebullition fluxes in high-latitude lakes is of great importance.

A novel strategy for estimating biomass of submerged aquatic vegetation in lake integrating UAV and Sentinel data.

Submerged aquatic vegetation (SAV) plays a fundamental ecological role in mediating carbon cycling within lakes, and its biomass is essential to assess the carbon sequestration potential of lake ecosystems. Remote sensing (RS) offers a powerful tool for large-scale SAV biomass retrieval. Given the underwater location of SAV, the spectral signal in RS data often exhibits weakness, capturing primarily horizontal structure rather than volumetric information crucial for biomass assessment. Fortunately, easily-measured SAV coverage can serve as an intermediary variable for difficultly-quantified SAV biomass inversion. Nevertheless, obtaining enough SAV coverage samples matching satellite image pixels for robust model development remains problematic. To overcome this challenge, we employed a UAV to acquire high-precision data, thereby replacing manual SAV coverage sample collection. In this study, we proposed an innovative strategy integrating unmanned aerial vehicle (UAV) and satellite data to invert large-scale SAV coverage, and subsequently estimate the biomass of the dominant SAV population (Potamogeton pectinatus) in Ulansuhai Lake. Firstly, a coverage-biomass model (R2 = 0.93, RMSE = 0.8 kg/m2) depicting the relationship between SAV coverage and biomass was developed. Secondly, in a designed experimental area, a high-precision multispectral image was captured by a UAV. Based on the Normalized Difference Water Index (NDWI), the UAV-based image was classified into non-vegetated and vegetated areas, thereby generating an SAV distribution map. Leveraging spatial correspondence between satellite pixels and the UAV-based SAV distribution map, the proportion of SAV within each satellite pixel, referred to as SAV coverage, was computed, and a coverage sample set matched with satellite pixels was obtained. Subsequently, based on the sample set, a satellite-scale SAV coverage estimation model (R2 = 0.78, RMSE = 14.05 %) was constructed with features from Sentinel-1 and Sentinel-2 data by XGBoost algorithm. Finally, integrating the coverage-biomass model with the obtained coverage inversion results, fresh biomass of SAV in Ulansuhai Lake was successfully estimated to be approximately 574,600 tons.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.

Management actions mitigate the risk of carbon dioxide emissions from urban lakes.

Lakes are recognized as important sources of carbon dioxide (CO2) emissions, which vary greatly across land use type. However, CO2 emissions from lakes in urban landscapes are generally overlooked despite their daily connections to human activity. Furthermore, the role of management actions in CO2 emissions remained unclear mostly because of the lack of long-term observations. Here, the CO2 partial pressure (pCO2) from two urban lakes (Lake Wuli and Lake Donghu) in eastern China were investigated based on 16-year (2002-2017) field measurements. This long-term measurements showed the annual mean pCO2 were 1150 ± 612 µatm for Lake Wuli and 1143 ± 887 µatm for Lake Donghu, with corresponding estimated flux of 21.12 ± 19.60 mmol m-2 d-1 and 16.42 ± 20.39 mmol m-2 d-1, respectively. This indicates significant CO2 evasion into the atmosphere. Strong links between CO2 and human-derived nutrients (e.g., ammonium) and dissolved organic carbon, dissolved oxygen, and trophic state index were found. Although pCO2 was relatively uniform across sites and seasons in each lake, substantial inter-annual variability with significant decreasing trends were found. The decrease in annual CO2 can be partly explained by the reduction of pollutant loadings with management actions, which held the hypotheses that management actions mitigated the CO2 emission risks. Overall, management actions (e.g., ecological restoration and municipal engineering) should be considered for better understanding the roles of anthropogenic aquatic ecosystems in carbon cycle.

Anthropogenic CO2 emission reduction during the COVID-19 pandemic in Nanchang City, China.

China is the largest CO2 emitting country on Earth. During the COVID-19 pandemic, China implemented strict government control measures on both outdoor activity and industrial production. These control measures, therefore, were expected to significantly reduce anthropogenic CO2 emissions. However, large discrepancies still exist in the estimated anthropogenic CO2 emission reduction rate caused by COVID-19 restrictions, with values ranging from 10% to 40% among different approaches. Here, we selected Nanchang city, located in eastern China, to examine the impact of COVID-19 on CO2 emissions. Continuous atmospheric CO2 and ground-level CO observations from January 1st to April 30th, 2019 to 2021 were used with the WRF-STILT atmospheric transport model and a priori emissions. And a multiplicative scaling factor and Bayesian inversion method were applied to constrain anthropogenic CO2 emissions before, during, and after the COVID-19 pandemic. We found a 37.1-40.2% emission reduction when compared to the COVID-19 pandemic in 2020 with the same period in 2019. Carbon dioxide emissions from the power industry and manufacturing industry decreased by 54.5% and 18.9% during the pandemic period. The power industry accounted for 73.9% of total CO2 reductions during COVID-19. Further, emissions in 2021 were 14.3-14.9% larger than in 2019, indicating that economic activity quickly recovered to pre-pandemic conditions.

[Optical Composition and Potential Driving Factors of Chromophoric Dissolved Organic Matter in Large Lakes and Reservoirs in the Eastern Region of China].

Intensified urbanization has been occurring in the eastern region of China in recent decades, and excessive industrial and household sewage has been discharged into lakes and reservoirs, which has directly lowered water quality and destructed the functions of aquatic ecosystems. Lakes and reservoirs are typically drinking water sources supplying water for metropolitan areas as well as large- and medium-sized cities. Chromophoric dissolved organic matter (CDOM) is the colored fraction of DOM, and its source and optical composition strongly affect water supply safety and the health of surrounding citizens. In April 2021, we collected 68 samples from Reservoir Changtan (n=11), Lake Taihu (n=25), Lake Hongze (n=18), and Lake Gaoyou (n=14), and we further carried out 28 days of laboratory bio-incubation, together with optical measurements and parallel factor analysis (EEMs-PARAFAC) to analyze the bio-degradability ω(BDOC), sources, and optical composition of CDOM in these waters. The results showed that after 28 days of laboratory bio-incubation, the bioavailability of dissolved organic carbon (BDOC) of the four lakes and reservoirs were all higher than 50%. PARAFAC results showed that CDOM collected from the four lakes was composed of four fluorescent components, including a terrestrial humic-like C1, a tryptophan-like C2, and tyrosine-like C3 and C4. Protein-like components (C2, C3, and C4) contributed importantly to the CDOM pool in the four waterbodies, and in Lake Hongze the contribution of C2-C4 was as high as (90.0±2.2)%. In Lake Hongze, total phosphorus (TP) correlated closely with C1-C3, indicating that those components can be used to trace the variability of TP. Among the four waterbodies, a254 was positively correlated with DOC (R2=0.96, P<0.01), indicating that a254 can be used to estimate the dynamics of DOC in these waters. In Lake Taihu, we found a low level of humification index (HIX) and a high level of biological index (BIX), indicating that autochthonous substances contributed importantly to the CDOM pool in this lake. In comparison, allochthonous sources contributed importantly to the CDOM pool in the remaining three waters. The enhanced monitoring of the discharge of industrial and agricultural effluents in the upstream watersheds of the four waters can help to protect the water quality and maintain healthy aquatic ecosystems.

Design, synthesis and evaluation of the Brigatinib analogues as potent inhibitors against tertiary EGFR mutants (EGFRdel19/T790M/C797S and EGFRL858R/T790M/C797S).

Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated encouraging clinical outcomes for patients with EGFR-mutated non-small cell lung cancer, a considerable number of patients will develop drug resistance and eventually undergo disease progression after taking EGFR-TKIs for a period of time. EGFRdel19/T790M/C797S and EGFRL858R/T790M/C797S are two most prevalent tertiary EGFR mutants identified in Osimertinib-resistant tumors and currently there is no therapy approved clinically targeting these mutants. In this study, we designed and synthesized a series of novel 4th generation EGFR inhibitors based on scaffold of Brigatinib. After extensive SAR studies, compound 23, the most promising candidate, exhibited strong biochemical potencies against EGFRdel19/T790M/C797S, EGFRL858R/T790M/C797S and other clinically relevant EGFR mutants while sparing wild type EGFR. In cellular assays, compound 23 potently inhibited proliferation of BaF3EGFR del19/T790M/C797S and PC-9EGFR del19/T790M/C797S. Moreover, compound 23 demonstrated good DMPK profile in mouse PK study.

A highly agricultural river network in Jurong Reservoir watershed as significant CO2 and CH4 sources.

Freshwaters are receiving growing concerns on atmospheric carbon dioxide (CO2) and methane (CH4) budget; however, little is known about the anthropogenic sources of CO2 and CH4 from river network in agricultural-dominated watersheds. Here, we chose such a typical watershed and measured surface dissolved CO2 and CH4 concentrations over 2 years (2015-2017) in Jurong Reservoir watershed for different freshwater types (river network, ponds, reservoir, and ditches), which located in Eastern China and were impacted by agriculture with high fertilizer N application. Results showed that significantly higher gas concentrations occurred in river network (CO2: 112 ± 36 µmol L-1; CH4: 509 ± 341 nmol L-1) with high nutrient concentrations. Dissolved CO2 and CH4 concentrations were supersaturated in all of the freshwater types with peak saturation ratios generally occurring in river network. Temporal variations in the gas saturations were positively correlated with water temperature. The saturations of CO2 and CH4 were positively correlated with each other in river network, and both of these saturations were also positively correlated with nutrient loadings, and negatively correlated with dissolved oxygen concentration. The highly agricultural river network acted as significant CO2 and CH4 sources with estimated emission fluxes of 409 ± 369 mmol m-2 d-1 for CO2 and 1.6 ± 1.2 mmol m-2 d-1 for CH4, and made a disproportionately large, relative to the area, contribution to the total aquatic carbon emission of the watershed. Our results suggested the aquatic carbon emissions accounted for 6% of the watershed carbon budget, and fertilizer N and watersheds land use played a large role in the aquatic carbon emission.

Urban rivers are hotspots of riverine greenhouse gas (N2O, CH4, CO2) emissions in the mixed-landscape chaohu lake basin.

Growing evidence shows that riverine networks surrounding urban landscapes may be hotspots of riverine greenhouse gas (GHG) emissions. This study strengthens the evidence by investigating the spatial variability of diffusive GHG (N2O, CH4, CO2) emissions from river reaches that drain from different types of landscapes (i.e., urban, agricultural, mixed, and forest landscapes), in the Chaohu Lake basin of eastern China. Our results showed that almost all the rivers were oversaturated with dissolved GHGs. Urban rivers were identified as emission hotspots, with mean fluxes of 470 µmol m-2d-1 for N2O, 7 mmol m-2d-1 for CH4, and 900 mmol m-2d-1 for CO2, corresponding to ~14, seven, and two times of those from the non-urban rivers in the Chaohu Lake basin, respectively. Factors related to the high N2O and CH4 emissions in urban rivers included large nutrient supply and hypoxic environments. The factors affecting CO2 were similar in all the rivers, which were temperature-dependent with suitable environments that allowed rapid decomposition of organic matter. Overall, this study highlights that better recognition of the influence that river networks have on global warming is required-particularly when it comes to urban rivers, as urban land cover and populations will continue to expand in the future. Management measures should incorporate regional hotspots to more efficiently mitigate GHG emissions.

The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years.

Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.

Satellite Estimation of Dissolved Carbon Dioxide Concentrations in China's Lake Taihu.

Lakes play an important role in the global carbon cycle; however, there are still large uncertainties in the estimation of global lake carbon emission due to the limitations in conducting field surveys at large geographic scales. Using long-term Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and field observation data in eutrophic Lake Taihu, we developed a novel approach to estimate the concentration of dissolved carbon dioxide (cCO2) in lakes. Based on the MODIS-derived chlorophyll-a concentration, lake surface temperature, diffuse attenuation coefficient of photosynthetically active radiation, and photosynthetically active radiation, a spatially explicit cCO2 model was developed using multivariate quadratic polynomial regression (coefficient of determination (R2) = 0.84, root-mean-square error (RMSE) = 11.81 µmol L-1, unbiased percent difference (UPD) = 22.46%). Monte Carlo simulations indicated that the model is stable with relatively small deviations in cCO2 estimates caused by input variables (UPD = 26.14%). MODIS data from 2003 to 2018 showed a significant declining trend (0.42 µmol L-1 yr-1, p < 0.05) in the annual mean cCO2. This was associated with a complex balance between the increasing algae biomass and decreasing external inputs of inorganic carbon, nutrients, and organic matter. The high spatiotemporal variabilities in cCO2 were attributed to river inputs and seasonal changes in temperature and algae biomass. The study shows that satellite remote sensing can play an important role in the field of inland water carbon cycling, providing timely much-needed insights into the drivers of the spatial and temporal changes in dissolved CO2 concentrations in inland waters.

Surface nitrous oxide (N2O) concentrations and fluxes from different rivers draining contrasting landscapes: Spatio-temporal variability, controls, and implications based on IPCC emission factor.

Increasing indirect nitrous oxide (N2O) emission from river networks as a result of enhanced human activities on landscapes has become a global issue, as N2O has been widely recognized as an important ozone-depleting greenhouse gas. However, indirect N2O emissions from different rivers, particularly for those that drain completely different landscapes, are poorly understood. Here, we investigated the spatial-temporal variability of N2O emissions among the different rivers in the Chaohu Lake Basin of Eastern China. Our results showed that river reaches in urban watersheds are the hotspots of N2O production, with a mean N2O concentration of ∼410 nmol L-1, which is 9-18 times greater than those mainly draining forested (23 nmol L-1), agricultural (42 nmol L-1) and mixed (45 nmol L-1) landscapes. Riverine dissolved N2O was generally supersaturated with respect to the atmosphere. Such N2O saturation can best be explained by nitrogen availability, except for those in the forested watersheds, where dissolved oxygen is thought to be the primary predictor. The estimated N2O fluxes in urban rivers reached ∼471 µmol m-2 d-1, a value of ∼22, 13, and 11 times that in forested, agricultural and mixed watersheds, respectively. Averaged riverine N2O emission factors (EF5r) of the forested, agricultural, urban and mixed watersheds were 0.066%, 0.12%, 0.95% and 0.16%, respectively, showing different deviations from the default EF5r that released by IPCC in 2019. This points to a need for more field measurements with wider spatial coverage and finer frequency to further refine the EF5r and to better reveal the mechanisms behind indirect N2O emissions as influenced by watershed landscapes.

Are nitrous oxide emissions indirectly fueled by input of terrestrial dissolved organic nitrogen in a large eutrophic Lake Taihu, China?

Lakes actively transform nitrogen (N) and emit disproportionately large amounts of N2O relative to their surface area. Studies have investigated the relative importance of denitrification or nitrification on N2O emissions; however, the linkage between N2O efflux and dissolved organic nitrogen (DON) and carbon (DOC) remains largely unknown. Long-term (2012-2017) seasonal field observations and a series of degradation experiments were used to unravel how DON composition impacts N2O emissions from Lake Taihu, China. In the northwestern part of the lake, large riverine inflow and high N2O emissions occur in all seasons (24.6 ± 25.2 µmol m-2 d-1), coincident with high levels of terrestrial DON and DOC here. The degradation of labile DON and DOC likely enhanced ammonification as supported by the correlations between NH4+-N and DON, DOC, a(350), and terrestrial humic-like C3. The area with large riverine inputs in the northwestern part of the lake was characterized by low DO which may enhance incomplete aerobic nitrification and incomplete denitrification, both leading to N2O production. Twenty days laboratory experiments indicated greater N2O production in the northwest inflow samples (N2O on day 20: 120.9 nmol L-1 and 17.3 nmol L-1 for bio- and photo-degradation samples, respectively) compared with the central lake samples (N2O on day 20: 20.3 nmol L-1 and 12.3 nmol L-1 for bio- and photo-degradation samples, respectively), despite both having low Chl-a. Our DON and DOC degradation experiments confirmed the occurrence of ammonification along with consumption of NH4+-N and thereafter NO3--N. Our results collectively suggest that terrestrial DON fueled ammonification, enhanced nitrification and incomplete denitrification, and thereby became an important contributor to the N2O efflux from Lake Taihu.

Environmental investments decreased partial pressure of CO2 in a small eutrophic urban lake: Evidence from long-term measurements.

Inland waters emit large amounts of carbon dioxide (CO2) to the atmosphere, but emissions from urban lakes are poorly understood. This study investigated seasonal and interannual variations in the partial pressure of CO2 (pCO2) and CO2 flux from Lake Wuli, a small eutrophic urban lake in the heart of the Yangtze River Delta, China, based on a long-term (2000-2015) dataset. The results showed that the annual mean pCO2 was 1030 ± 281 µatm (mean ± standard deviation) with a mean CO2 flux of 1.1 ± 0.6 g m-2 d-1 during 2000-2015, suggesting that compared with other lakes globally, Lake Wuli was a significant source of atmospheric CO2. Substantial interannual variability was observed, and the annual pCO2 exhibited a decreasing trend due to improvements in water quality driven by environmental investment. Changes in ammonia nitrogen and total phosphorus concentrations together explained 90% of the observed interannual variability in pCO2 (R2 = 0.90, p < 0.01). The lake was dominated by cyanobacterial blooms and showed nonseasonal variation in pCO2. This finding was different from those of other eutrophic lakes with seasonal variation in pCO2, mostly because the uptake of CO2 by algal-derived primary production was counterbalanced by the production of CO2 by algal-derived organic carbon decomposition. Our results suggested that anthropogenic activities strongly affect lake CO2 dynamics and that environmental investments, such as ecological restoration and reducing nutrient discharge, can significantly reduce CO2 emissions from inland lakes. This study provides valuable information on the reduction in carbon emissions from artificially controlled eutrophic lakes and an assessment of the impact of inland water on the global carbon cycle.

Eutrophic Lake Taihu as a significant CO2 source during 2000-2015.

Inland lakes receive growing attentions on eutrophication and their roles in global carbon cycle. However, understanding how inland lakes contribute to global carbon cycle is seriously hampered due to a shortage of long-term records. This study investigated the carbon dioxide (CO2) flux from the Lake Taihu, a large (2400 km2) and shallow (mean depth 1.9 m) eutrophic lake in subtropical region, based on a long-term (2000-2015) measurement of the partial pressure of carbon dioxide (pCO2) at high spatiotemporal resolution. We found that the Lake Taihu was a significant source of atmospheric CO2 with an average CO2 emission flux at 18.2 ± 8.4 mmol m-2 d-1 (mean±1standard deviation) and a mean annual pCO2 value of 778 ± 169 µatm. The highest pCO2 and CO2 flux were observed in eutrophic zone with a high external input of carbon and nutrient, and the lowest in non-eutrophic zones with no direct external input of nutrient and carbon. A substantial seasonal pattern in pCO2 was observed, particularly in eutrophic pelagic area, and was significantly negatively correlated with chlorophyll a. Long-term measurement showed the interannual variation in annual lake CO2 dynamics, which was highly sensitive to human-induced nutrient input. Watershed input of carbon and nutrient leads to the high CO2 level, counterbalancing the in-lake primary production. All lines of evidence suggest that human activities may have predominate contribution to CO2 source in the Lake Taihu, and this mechanism might be widespread in global freshwater lakes.